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Word Embeddings Versus LDA for Topic Assignment in Documents

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Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10449))

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Abstract

Topic assignment for a corpus of documents is a task of natural language processing (NLP). One of the noted and well studied methods is Latent Dirichlet Allocation (LDA) where statistical methods are applied. On the other hand applying deep-learning paradigm proved useful for many NLP tasks such as classification [3], sentiment analysis [8], text summarization [11]. This paper compares the results of LDA method and application of representations provided by Word2Vec [5] which makes use of deep learning paradigm.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics, Physics and Informatics, Institute of Informatics, University of Gdansk, 80-308, Gdansk, Poland

    Joanna Jȩdrzejowicz & Magdalena Zakrzewska

Authors
  1. Joanna Jȩdrzejowicz
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  2. Magdalena Zakrzewska
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Corresponding author

Correspondence to Magdalena Zakrzewska .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Jȩdrzejowicz, J., Zakrzewska, M. (2017). Word Embeddings Versus LDA for Topic Assignment in Documents. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_34

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  • DOI: https://doi.org/10.1007/978-3-319-67077-5_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67076-8

  • Online ISBN: 978-3-319-67077-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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